{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import sys\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "font_scale = 4.0\n",
    "plt.rcParams[\"font.family\"]      = \"serif\"\n",
    "plt.rcParams[\"font.serif\"]       = \"Times New Roman\"\n",
    "plt.rcParams[\"mathtext.rm\"]      = \"serif\"\n",
    "plt.rcParams[\"mathtext.it\"]      = \"serif:italic\"\n",
    "plt.rcParams[\"mathtext.bf\"]      = \"serif:bold\"\n",
    "plt.rcParams[\"mathtext.fontset\"] = \"custom\"\n",
    "plt.rcParams[\"xtick.direction\"]  = \"in\"\n",
    "plt.rcParams[\"ytick.direction\"]  = \"in\"\n",
    "plt.rcParams[\"xtick.labelsize\"]  = 6.0*font_scale\n",
    "plt.rcParams[\"ytick.labelsize\"]  = 6.0*font_scale\n",
    "plt.rcParams[\"axes.linewidth\"]   = 0.5*font_scale\n",
    "plt.rcParams[\"axes.labelsize\"]   = 9.0*font_scale\n",
    "plt.rcParams[\"axes.titlesize\"]   = 9.0*font_scale\n",
    "plt.rcParams[\"font.size\"]        = 9.0*font_scale\n",
    "plt.rcParams[\"legend.fontsize\"]  = 5.0*font_scale\n",
    "plt.rcParams[\"xtick.top\"]    = True\n",
    "plt.rcParams[\"xtick.bottom\"] = True \n",
    "plt.rcParams[\"ytick.left\"]   = True \n",
    "plt.rcParams[\"ytick.right\"]  = True\n",
    "plt.rcParams[\"xtick.minor.top\"]    = True\n",
    "plt.rcParams[\"xtick.minor.bottom\"] = True \n",
    "plt.rcParams[\"ytick.minor.left\"]   = True \n",
    "plt.rcParams[\"ytick.minor.right\"]  = True\n",
    "plt.rcParams[\"xtick.minor.visible\"] = True\n",
    "plt.rcParams[\"ytick.minor.visible\"] = True\n",
    "plt.rcParams[\"xtick.major.size\"] = 3.0*font_scale\n",
    "plt.rcParams[\"ytick.major.size\"] = 3.0*font_scale\n",
    "plt.rcParams[\"xtick.minor.size\"] = 1.5*font_scale\n",
    "plt.rcParams[\"ytick.minor.size\"] = 1.5*font_scale\n",
    "plt.rcParams[\"xtick.major.width\"] = 0.5*font_scale\n",
    "plt.rcParams[\"ytick.major.width\"] = 0.5*font_scale\n",
    "plt.rcParams[\"xtick.minor.width\"] = 0.35*font_scale\n",
    "plt.rcParams[\"ytick.minor.width\"] = 0.35*font_scale"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'NFW enclosed mass profile')"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1296x504 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "\"\"\"\n",
    "sph_profile.py\n",
    "\n",
    "Purpose: \n",
    "        This procedure is designed to generate the matter distribution of the galaxy \n",
    "        according to the commonly used parametric models under spherical symmetry.\n",
    "         \n",
    "Calling Sequence:\n",
    "        from sph_profile import sph_profile as sph\n",
    "        test_profile = sph(r,dim=\"3D\")\n",
    "        \n",
    "        r: float or array_like with shape (n,), in kpc\n",
    "            the radius where we calculate the matter distribution\n",
    "        dim: {'2D', '3D'}, optional\n",
    "            dim='2D', projected matter distribution\n",
    "            dim='3D', intrinsic matter distribution\n",
    "Methods:\n",
    "****************************************************************************\n",
    "        test_profile.power_law(rho_0,r_s,a)\n",
    "        \n",
    "        power law matter density distribution\n",
    "        origin form in 3D: rho_0*(r/r_s)**(-a) \n",
    "        \n",
    "        rho_0: float, in M_sun/kpc^3\n",
    "            the intrinsic density at the scale radius\n",
    "        r_s: float, in kpc\n",
    "            the scale radius\n",
    "        a: float, dimensionless, from 1.0 to 3.0\n",
    "            the logarithmic slope of the intrinsic density profile\n",
    "            a=2.0, isothermal model\n",
    "            \n",
    "        return: float or array_like with shape (n,)\n",
    "            dim='2D', return to projected density distribution in M_sun/kpc^2\n",
    "            dim='3D', return to intrinsic density distribution in M_sun/kpc^3\n",
    "****************************************************************************\n",
    "        test_profile.M_power_law(rho_0,r_s,a)\n",
    "        \n",
    "        power law enclosed mass distribution\n",
    "        \n",
    "        rho_0, r_s, a: defined as the same as test_profile.power_law(rho_0,r_s,a)\n",
    "            \n",
    "        return: float or array_like with shape (n,)\n",
    "            dim='2D', return to projected enclosed mass in M_sun within r\n",
    "            dim='3D', return to intrinsic enclosed mass in M_sun within r\n",
    "****************************************************************************\n",
    "        test_profile.two_power_law(rho_0,r_s,a,b)\n",
    "        \n",
    "        double power law matter density distribution\n",
    "        origin form in 3D: rho_0*(r/r_s)^(-a)*(1+r/r_s)^(a-b)\n",
    "        \n",
    "        rho_0: float, in M_sun/kpc^3\n",
    "            the 2^(b-a) times of intrinsic density at the scale radius\n",
    "        r_s: float, in kpc\n",
    "            the scale radius\n",
    "        a,b: float, dimensionless, a<3.0,b>=3.0\n",
    "            a is the logarithmic inner slope of the intrinsic density profile\n",
    "            b is the logarithmic outer slope of the intrinsic density profile\n",
    "            a=1.0,b=3.0, NFW model\n",
    "            a=1.0,b=4.0, Hernquist model\n",
    "        return: float or array_like with shape (n,)\n",
    "            dim='2D', return to projected density distribution in M_sun/kpc^2\n",
    "            dim='3D', return to intrinsic density distribution in M_sun/kpc^3\n",
    "****************************************************************************\n",
    "        test_profile.M_two_power_law(rho_0,r_s,a,b)\n",
    "        \n",
    "        double power law enclosed mass distribution\n",
    "        \n",
    "        rho_0, r_s, a, b: defined as the same as test_profile.two_power_law(rho_0,r_s,a,b)\n",
    "            \n",
    "        return: float or array_like with shape (n,)\n",
    "            dim='2D', return to projected enclosed mass in M_sun within r\n",
    "            dim='3D', return to intrinsic enclosed mass in M_sun within r\n",
    "****************************************************************************\n",
    "        test_profile.sersic(L0,Re,m)\n",
    "        \n",
    "        sersic distribution\n",
    "        origin form in 2D: see Eq.2 in https://arxiv.org/pdf/2311.07442 \n",
    "        \n",
    "        L_0: float, in M_sun or L_sun\n",
    "            total mass or luminosity of the sersic component\n",
    "        Re: float, in kpc\n",
    "            the projected radius enclosed half of L_0\n",
    "        m: float, dimensionless\n",
    "            the sersic index of the sersic component \n",
    "            m=1.0, exponential disk model\n",
    "            m=4.0, de Vaucouleurs bulge model\n",
    "        return: float or array_like with shape (n,)\n",
    "            dim='2D', return to projected density distribution in M_sun/kpc^2 or L_sun/kpc^2\n",
    "            dim='3D', return to intrinsic density distribution in M_sun/kpc^3 or L_sun/kpc^2\n",
    "****************************************************************************\n",
    "        test_profile.M_sersic(L0,Re,m)\n",
    "        \n",
    "        sersic enclosed mass or luminosity distribution\n",
    "        \n",
    "        L0, Re, m: defined as the same as test_profile.sersic(L0,Re,m)\n",
    "            \n",
    "        return: float or array_like with shape (n,)\n",
    "            dim='2D', return to projected enclosed mass or luminosity in M_sun or L_sun within r\n",
    "            dim='3D', return to intrinsic enclosed mass or luminosity in M_sun or L_sun within r\n",
    "\"\"\"\n",
    "# example to generate standard NFW profile\n",
    "# sys.path.append(file_dir)   add the path of the sph_profile.py\n",
    "from sph_profile import sph_profile as sph\n",
    "\n",
    "r = np.logspace(-2,2)    # radii in kpc\n",
    "M_dm5 = 1e11    # projected halo mass in M_sun within 5kpc\n",
    "rs = 30.0    # halo scale radius in kpc\n",
    "a,b = 1.0,3.0   # inner & outer slope \n",
    "rho_0 = M_dm5/sph(5.0,dim=\"2D\").M_two_power_law(1.0,rs,a,b)\n",
    "rho_nfw = sph(r,dim=\"3D\").two_power_law(rho_0,rs,a,b)\n",
    "M_nfw = sph(r,dim=\"3D\").M_two_power_law(rho_0,rs,a,b)\n",
    "\n",
    "fig,ax = plt.subplots(1,2,figsize=(18,7))\n",
    "fig.subplots_adjust(wspace=0.3)\n",
    "ax[0].plot(r,rho_nfw)\n",
    "ax[0].set_xscale(\"log\")\n",
    "ax[0].set_yscale(\"log\")\n",
    "ax[0].set_xlabel(r\"$r[\\rm kpc]$\")\n",
    "ax[0].set_ylabel(r\"$\\rho[\\rm M_\\odot kpc^{-3}]$\")\n",
    "ax[0].set_title(\"NFW density profile\")\n",
    "\n",
    "ax[1].plot(r,M_nfw)\n",
    "ax[1].set_xscale(\"log\")\n",
    "ax[1].set_yscale(\"log\")\n",
    "ax[1].set_xlabel(r\"$r[\\rm kpc]$\")\n",
    "ax[1].set_ylabel(r\"$M[\\rm M_\\odot]$\")\n",
    "ax[1].set_title(\"NFW enclosed mass profile\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "theta_ein is 0.15arcsec\n"
     ]
    }
   ],
   "source": [
    "\"\"\"\n",
    "sph_lensing.py\n",
    "\n",
    "Purpose: \n",
    "        This procedure is designed to mock the observables of spherical lensing system\n",
    "        according to given parametric models included in sph_profile.\n",
    "         \n",
    "Calling Sequence:\n",
    "        from sph_lensing import sph_lensing as sph_lens\n",
    "        test_lens = sph_lens(lens_specification)\n",
    "        \n",
    "        lens_specification: dictionary \n",
    "            to store all parameters used in this routine\n",
    "            \n",
    "Parameters:\n",
    "        The parameters in lens_specification\n",
    "        \n",
    "        zd: float, dimensionless\n",
    "            the redshift of deflector \n",
    "        zs: float, dimensionless\n",
    "            the redshift of source\n",
    "        cosmo: None or object in astropy.cosmology\n",
    "            the cosmology settings based on astropy.cosmology FlatLambdaCDM(H0,Om0)\n",
    "            H0: float, Hubble constant in km/s/Mpc\n",
    "            Om0: float, the matter fraction at z=0\n",
    "            if this parameter is None, the cosmology will follow the results of Planck2016 \n",
    "        mass_model_list: list of str with shape (m,)\n",
    "            the names of the models used to describe the mass distribution of deflector\n",
    "            including \"power_law\", \"two_power_law\", \"sersic\"\n",
    "            e.g., double sersic for stars + NFW for dark matter halo\n",
    "            mass_model_list = [\"sersic\",\"sersic\",\"two_power_law\"]\n",
    "        mass_model_para: list of parameters associate with mass_model_list \n",
    "            the parameter list for the models included in mass_model_list \n",
    "            in the example above double sersic for stars + NFW for dark matter halo\n",
    "            mass_model_para = [[L1,Re1,m1],[L2,Re2,m2],[rho_dm,r_dm,1.0,3.0]]\n",
    "        \n",
    "Methods:\n",
    "****************************************************************************\n",
    "        test_lens.mock_einstein_radius(R_min = 0.05,R_max = 30.0)\n",
    "        \n",
    "        to find the angular Einstein radius of deflector specified by lens_specification\n",
    "        \n",
    "        R_min: float, in kpc\n",
    "            the lower limit of the Einstein radius, default 0.05kpc\n",
    "        R_max: float, in kpc\n",
    "            the upper limit of the Einstein radius, default 30.0kpc\n",
    "            \n",
    "        return: float\n",
    "            the Einstein radius in arcsec \n",
    "****************************************************************************\n",
    "        test_lens.mock_secondary_parameter()\n",
    "        \n",
    "        to calculate the lens secondary parameter, see https://arxiv.org/pdf/1911.05083v1\n",
    "        this quantity can only by obtained after the Einstein radius has been known\n",
    "        \n",
    "        return: float \n",
    "            the dimensionless number, \n",
    "            if lens mass model is a single power law, Xi=2(a-2) where a is density slope\n",
    "****************************************************************************\n",
    "        test_lens.get_einstein_radius(theta_ein)\n",
    "        \n",
    "        to utilize the observed value of Einstein radius to avoid solving lensing equation\n",
    "        \n",
    "        theta_ein: float, in arcsec\n",
    "            the observed Einstein radius\n",
    "        \n",
    "        return: float\n",
    "            the Einstein radius in arcsec\n",
    "\"\"\"\n",
    "\n",
    "\n",
    "# obtain the Einstein radius of above NFW halo\n",
    "from astropy.cosmology import FlatLambdaCDM\n",
    "from sph_lensing import sph_lensing as sph_lens\n",
    "lens_specification = {\n",
    "    \"zd\":0.5,\n",
    "    \"zs\":1.5,\n",
    "    \"cosmo\":FlatLambdaCDM(70.0,0.3),\n",
    "    \"mass_model_list\":[\"two_power_law\"],\n",
    "    \"mass_model_para\":[[rho_0,rs,a,b]]}\n",
    "lens_nfw=sph_lens(lens_specification)\n",
    "theta_ein=lens_nfw.mock_einstein_radius()\n",
    "print(\"theta_ein is {0:.2f}arcsec\".format(theta_ein))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "\"\"\"\n",
    "sph_jeans.py\n",
    "\n",
    "Purpose: \n",
    "        This procedure is designed to claculate the spherical Jeans equation,\n",
    "        according to given parametric tracer and mass models included in sph_profile.\n",
    "         \n",
    "Calling Sequence:\n",
    "        from sph_jeans import sph_jeans as sph_dyn\n",
    "        test_dyn = sph_dyn(R,dyn_specification)\n",
    "        \n",
    "        R: float, in kpc\n",
    "            the radius where we calculate the LOS velocity dispersion\n",
    "        lens_specification: dictionary \n",
    "            to store all parameters used in this routine\n",
    "            \n",
    "Parameters:\n",
    "        The parameters in lens_specification\n",
    "        \n",
    "        massless_tracer: bool\n",
    "            if True, the tracer is massless, and has no contribution to the total mass model\n",
    "            if Flase, the tracer has contribution to the total mass model\n",
    "        polynomial: {\"legendre\",\"laguerre\"}, optional\n",
    "            the polynomial to calculate the integral when solving Jeans equation\n",
    "            Legendre polynomial is recommended.\n",
    "        npoly: int\n",
    "            the order of the ploynomial\n",
    "        anisotropy: {\"const\",\"om\"}, optional\n",
    "            anisotropy=\"const\", beta is a constant number from -inf to 1.0\n",
    "            anisotropy=\"om\", beta is Osipkov-Merritt model\n",
    "        tracer_model_list: list of str with shape (m,)\n",
    "            the names of the models used to describe the tracer distribution\n",
    "            including \"power_law\", \"two_power_law\", \"sersic\"\n",
    "            e.g., double sersic for stars as the tracer\n",
    "            tracer_model_list = [\"sersic\",\"sersic\"]\n",
    "        tracer_model_para: list of parameters associate with tracer_model_list \n",
    "            the parameter list for the models included in tracer_model_list \n",
    "            in the example above double sersic for stars as the tracer\n",
    "            tracer_model_para = [[L1,Re1,m1],[L2,Re2,m2]]\n",
    "        mass_model_list: list of str with shape (N,)\n",
    "            the names of the models used to describe the total mass distribution \n",
    "            including \"power_law\", \"two_power_law\", \"sersic\"\n",
    "            e.g., total mass = double sersic for stars + NFW for dark matter halo\n",
    "            mass_model_list = [\"sersic\",\"sersic\",\"two_power_law\"]\n",
    "        mass_model_para: list of parameters associate with mass_model_list \n",
    "            the parameter list for the models included in mass_model_list \n",
    "            in the example above total mass = double sersic for stars + NFW for dark matter halo\n",
    "            mass_model_para = [[L1,Re1,m1],[L2,Re2,m2],[rho_dm,r_dm,1.0,3.0]]\n",
    "        tracer_model_label: list of bool,\n",
    "            the list containing labels associated with mass_model_list,\n",
    "            in the example above total mass = double sersic for stars + NFW for dark matter halo,\n",
    "            stellar component described by the double sersic model is tracer\n",
    "            and mass_model_list = [\"sersic\",\"sersic\",\"two_power_law\"]\n",
    "            so tracer_model_label = [True,True,False]\n",
    "        beta: None or float, dimensionless\n",
    "            the value of the constant anisotropy prameter, \n",
    "            if anisotropy=\"om\", this quantity will not be used.\n",
    "        ra: None or float, in kpc\n",
    "            the scale radius in Osipkov-Merritt model,\n",
    "            if anisotropy=\"const\", this quantity will not be used.\n",
    "Methods:\n",
    "****************************************************************************\n",
    "        test_dyn.solver()\n",
    "        \n",
    "        to solve the Jeans equation specified by dyn_specification\n",
    "            \n",
    "        return: Vrms, Sigma\n",
    "            Vrms, float in km/s, the velocity dispersion at radius R\n",
    "            Sigma, float in L_sun/kpc^2, the surface density of the tracer at radius R\n",
    "****************************************************************************\n",
    "        test_dyn.accelerated_solver(accelerated_nu_roots,accelerated_Mnu_roots)\n",
    "        \n",
    "        to solve the Jeans equation specified by dyn_specification during MCMC sampling\n",
    "        \n",
    "        accelerated_nu_roots: array_like with shape (npoly,), \n",
    "            the values of the tracer intrinsic density at the roots of polynomial applied, \n",
    "            it can be generated by test_dyn.acceleration.\n",
    "        accelerated_Mnu_roots: array_like with shape (npoly,), \n",
    "            the values of the tracer intrinsic enclosed mass at the roots of polynomial applied, \n",
    "            it can be generated by test_dyn.acceleration.\n",
    "            \n",
    "        return: Vrms, Sigma\n",
    "            Vrms, float in km/s, the velocity dispersion at radius R\n",
    "            Sigma, float in L_sun/kpc^2, the surface density of the tracer at radius R\n",
    "****************************************************************************\n",
    "        test_dyn.acceleration()\n",
    "        \n",
    "        to prepare the values of the tracer intrinsic density and enclosed mass before MCMC sampling.\n",
    "        attention: the calculation of intrinsic density of enclosed mass is time-consuming,  \n",
    "            but the values we concerned during quadrature only depend on the roots of polynomial,\n",
    "            therefore, we can prepare these MCMC-independent calculation in advance.\n",
    "            the example of this acceleration procedure is in the sph_dyn_lens_example.py!\n",
    "            when we only use this acceleration procedure, the mass_model_para can be None.\n",
    "        \n",
    "        accelerated_nu_roots, accelerated_Mnu_roots:\n",
    "            the value of tracer intrinsic density or enclosed mass at rt\n",
    "            polynomial = \"laguerre\", rt = R+roots\n",
    "            polynomial = \"legendre\", rt = R+(1.0+roots)/(1.0-roots) \n",
    "            roots are the points where P_nploy(roots) = 0\n",
    "            where P_npoly is the polynomial with order of npoly\n",
    "            \n",
    "        return: \n",
    "            if massless_tracer = True, return to accelerated_nu_roots\n",
    "            if massless_tracer = Flase, return to accelerated_nu_roots and accelerated_Mnu_roots\n",
    "\"\"\"\n",
    "\n",
    "# obtain the LOS velocity dispersion profile\n",
    "from sph_jeans import sph_jeans as sph_dyn\n",
    "rho0 = 1e11/(2*np.pi)  # rho0 in M_sun/kpc^3\n",
    "dyn_specification = {\n",
    "    \"massless_tracer\":False,\n",
    "    \"polynomial\":\"legendre\",\n",
    "    \"npoly\":30,\n",
    "    \"anisotropy\":\"const\",\n",
    "    \"tracer_model_list\":[\"two_power_law\"],\n",
    "    \"tracer_model_para\":[[rho0,1.0,1.0,4.0]],\n",
    "    \"tracer_model_label\":[True],\n",
    "    \"mass_model_list\":[\"two_power_law\"],\n",
    "    \"mass_model_para\":[[rho0,1.0,1.0,4.0]],\n",
    "    \"beta\":0.0,\n",
    "    \"ra\":None}\n",
    "R=np.logspace(-2,1,100)\n",
    "Vrms  = np.zeros_like(R)\n",
    "Sigma = np.zeros_like(R) \n",
    "for i in range(100):\n",
    "    dynamics=sph_dyn(R[i],dyn_specification)\n",
    "    Vrms[i],Sigma[i] = dynamics.solver()\n",
    "Vrms_iso = Vrms\n",
    "\n",
    "dyn_specification = {\n",
    "    \"massless_tracer\":False,\n",
    "    \"polynomial\":\"legendre\",\n",
    "    \"npoly\":30,\n",
    "    \"anisotropy\":\"const\",\n",
    "    \"tracer_model_list\":[\"two_power_law\"],\n",
    "    \"tracer_model_para\":[[rho0,1.0,1.0,4.0]],\n",
    "    \"tracer_model_label\":[True],\n",
    "    \"mass_model_list\":[\"two_power_law\"],\n",
    "    \"mass_model_para\":[[rho0,1.0,1.0,4.0]],\n",
    "    \"beta\":0.5,\n",
    "    \"ra\":None}\n",
    "R=np.logspace(-2,1,100)\n",
    "Vrms  = np.zeros_like(R)\n",
    "Sigma = np.zeros_like(R) \n",
    "for i in range(100):\n",
    "    dynamics=sph_dyn(R[i],dyn_specification)\n",
    "    Vrms[i],Sigma[i] = dynamics.solver()\n",
    "Vrms_rad = Vrms\n",
    "\n",
    "dyn_specification = {\n",
    "    \"massless_tracer\":False,\n",
    "    \"polynomial\":\"legendre\",\n",
    "    \"npoly\":30,\n",
    "    \"anisotropy\":\"const\",\n",
    "    \"tracer_model_list\":[\"two_power_law\"],\n",
    "    \"tracer_model_para\":[[rho0,1.0,1.0,4.0]],\n",
    "    \"tracer_model_label\":[True],\n",
    "    \"mass_model_list\":[\"two_power_law\"],\n",
    "    \"mass_model_para\":[[rho0,1.0,1.0,4.0]],\n",
    "    \"beta\":-0.5,\n",
    "    \"ra\":None}\n",
    "R=np.logspace(-2,1,100)\n",
    "Vrms  = np.zeros_like(R)\n",
    "Sigma = np.zeros_like(R) \n",
    "for i in range(100):\n",
    "    dynamics=sph_dyn(R[i],dyn_specification)\n",
    "    Vrms[i],Sigma[i] = dynamics.solver()\n",
    "Vrms_tan = Vrms"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7f975ae23fd0>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 576x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "G=4.301e-6    # gravitational constant in (km/s)^2*kpc/M_sun\n",
    "quad_color = [(209/255,41/255,32/255),(21/255,29/255,41/255),(18/255,80/255,123/255),(250/255,192/255,61/255)]\n",
    "Vcirc=np.sqrt(G*1e11)\n",
    "fig,ax = plt.subplots(1,1,figsize=(8,5))\n",
    "ax.plot(R,Vrms_iso/Vcirc,color=quad_color[3],label=r\"$\\beta=0$\")\n",
    "ax.plot(R,Vrms_rad/Vcirc,color=quad_color[0],label=r\"$\\beta=0.5$\")\n",
    "ax.plot(R,Vrms_tan/Vcirc,color=quad_color[2],label=r\"$\\beta=-0.5$\")\n",
    "ax.set_xscale(\"log\")\n",
    "ax.set_xlabel(r\"$R/r_{\\rm s}$\")\n",
    "ax.set_ylabel(r\"$\\sigma(R)/\\sqrt{GM/r_{\\rm s}}$\")\n",
    "ax.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
